Patient anatomy constantly changes and deforms throughout the course of a radiation treatment. This creates uncertainty in the delivered radiation dose and affects the treatment outcome. A substantial amount of research has been focused on developing deformation algorithms specifically for this issue. See e.g., Bajcsy, R. & Kovacic, S., 1989, Comput. Vis. Graph. Image Process. 46:1-21; Bookstein, F. L., 1989, IEEE Trans. Pattern Anal. Mach. Intell., 567-585; Davatzikos, C., et al., 1996, IEEE Trans. Med. Imaging 15:112-115; Meyer, C. R., et al., 1997, Med. Image Anal. 1:195-206; Thirion, J. P., 1998, Med. Image Anal. 2:243-260; D'Agostino, E., et al., 2003, Med. Image Anal. 7:565-575; Kybic, J. & Unser, M., 2003, IEEE Trans. Image Process. 12:1427-1442; Lu, W. G., et al., 2003, Phys. Med. Biol. 49:3067-3087; Coselmon, M. M., 2004, et al., Med. Phys. 31:2942-2948; Wang, H., et al., 2005, Phys. Med. Biol. 50:2887-2905; Brock, K. K., et al., 2005, Med. Phys. 32:1647-1659; Foskey, M., et al., 2005, Phys. Med. Biol. 50:5869-5892.
The resulting variety of these algorithms can produce distinctly different deformation predictions. Therefore, before the implementation of the deformation algorithms can reach its full potential, further work is needed to develop quality assurance techniques for verifying their accuracy. The radiographic phantom described herein addresses inter alia this effort.
Currently, there are methods to test the accuracy of deformation algorithms, but these methods have some shortcomings. These methods fall into three basic categories: contour comparison [Foskey, et al., 2005, Id.; Heath, E., et al., 2007, Med. Phys. 34:4409-4421; Serban, M., et al., 2008, Med. Phys. 35:1094-1102; Wijesooriya, K., et al., 2008, Med. Phys. 35, 1251-1260], landmark tracking [Meyer, C. R., et al., 1997, Id.; Heath, E., et al., 2007, Id.; Serban, M., et al., 2008, Id.; Kerdok, A. E., et al., 2001, “TruthCube: establishing physical standards for real time soft tissue simulation,” Proceedings of the Int. Workshop on Deformable Modeling and Soft Tissue Simulation, Bonn, Germany; Lu, W. G., et al., 2004, Id.; Coselmon, M. M., et al., 2004, Id.; Wang, H., et al., 2005, Id.; Brock, K. K., et al., 2005, Id.; Rietzel, E. & Chen, G. T., 2006, Med. Phys. 33:4423-4430; Kashani, R., et al., 2007, Med. Phys. 34:2785-2788], and simulated deformations [D'Agostino, E., et al., 2003, Id.; Kybic, J. & Unser, M., 2003, Id.; Lu, W. G., et al., 2004, Id.; Wang, H., et al., 2005, Id.]
In the first of these methods, surface contours are created for a volume on images before and after deformation. Then, the initial contours are deformed, according to a deformation algorithm, and compared to those created on the distorted image. Without wishing to be bound by any theory, it is believed that the use of this method for a quantitative comparison is problematic. For example, since contour comparison occurs between surfaces, deformation errors that are tangential to the surfaces are not measured. In addition, contour comparison does not directly determine deformation accuracy for the points inside the surface.
For the second test category, visible landmarks are either identified on an existing image or they are created with the implantation of radiopaque markers. The locations of these landmarks are measured before and after the image deformation. Then, these measurements can be compared to the predictions of a deformation algorithm. Without wishing to be bound by any theory, it is believed that at least one problem with this method is that any prominent landmark will also stand out to the algorithm. Thus, these points might not be representative of the deformation errors for the volume as a whole. The images can be processed to remove the markers prior to their use by an algorithm; however, even the processing remnants could affect the deformation predictions.
For the third test category, deformations are applied digitally to an image. Then, the applied deformation is compared directly to the algorithm predictions. The deformation can be based on a physical model of the system, but this requires a firm biomechanical understanding of the local anatomy. See e.g., Brock, K. K., et al., 2005, Id.; Bharatha, A., et al., 2001, Med. Phys. 28:2551-2560; Brock, K. K., et al., 2002, Med. Phys. 29:1403-1405; Werner, R., et al., 2009, Med. Phys. 36:1500-1511; Almayah, A., et al., 2010a, Med. Phys. 37:4560-4571; Almayah, A., et al., 2010b, Phys. Med. Biol. 55:6491-6500. Without wishing to be bound by any theory, it is believed that at least one issue with this method is that the deformed image would have the same noise variations as the original image, which would affect the determined deformation. Additional noise could be created on the deformed image, but this would not represent the discrepancies of two distinct images.
By the present application there are provided methods and devices to overcome the mentioned shortcomings of the verification techniques currently available by, e.g., reducing the anatomy and its deformations to a two-dimensional system. This system is visible through a transparent, e.g., acrylic, plate, which allows the deformation markers to be non-radiopaque. Thus, these markers will not appear on the scanned images and do not perturb the deformation algorithms. The transparency of the surface markers permits them to be placed at a high density and, therefore, also allows for a measurement of the complete deformation field, rather than just the deformation of a few points. Predicted deformations for the scanned images of the device can be directly compared to these measured deformations.
A two-dimensional phantom can be tailor-made to represent the imaging and elastic properties of any anatomical site. For example, the deformations measured by the phantom can be applied to actual patient CT images to create deformed CT images. These simulated deformations would not require detailed computational modeling in the formulation of a deformable registration comparison.